Statistical methodology in the health sciences

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The principal objective of my research is to develop and evaluate statistical methodology that may be usefully applied to health sciences research. Most of this research involves the development of methods that may be applied when standard statistical techniques are inappropriate, due to the violation of underlying assumptions. An example of this is my past and continuing research on methods of analysis for cluster randomization trials which allocate intact social units rather than individuals to treatment. Since individuals within a cluster cannot be regarded as statistically independent, standard methods of analysis do not apply, and could result in misleading conclusions. Much of my research in this area has been focused on the development of methods that take into account within-cluster dependencies in such a way that the resulting inferences have valid and efficient statistical properties. My textbook "Design and Analysis of Cluster Randomization Trials", co-authored with Neil Klar (Arnold Publishing Co., 2000) presents a comprehensive treatment of this subject.The Chinese translated edition of this text was published in 2006. A second example involves the development of inference methods for interobserver agreement in samples of small to moderate size. Although the kappa statistics is widely used in both psychology and the health sciences to measure interobserver agreement, traditional methods for significance-testing and confidence interval construction require sample sizes that are frequently beyond the range available to many investigators. My overall objective here has been to develop a unified framework for inferences concerning interobserver agreement that accommodates both multiple raters and multiple outcome categories. Virtually all the problems I address emanate from my experience as a biostatistician collaborating with researchers in the Health Sciences.